Gespeichert in:
| Weitere beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Gistrup, Denmark
River Publishers
2023
New York ; London Routledge |
| Schriftenreihe: | River Publishers series in automation, control and robotics
|
| Schlagwörter: | |
| Links: | https://ieeexplore.ieee.org/book/9997412 https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=7253760 https://ieeexplore.ieee.org/book/9997412 https://www.taylorfrancis.com/books/9781003393030 https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3619637 |
| Abstract: | Convolutional neural networks (CNNs), a type of deep neural network that has become dominant in a variety of computer vision tasks, in recent few years has attracted interest across a variety of domains due to their high efficiency at extracting meaningful information from visual imagery. Convolutional neural networks (CNNs) excel at a wide range of machine learning and deep learning tasks. As sensor-enabled internet of things (IoT) devices pervade every aspect of modern life, it is becoming increasingly critical to run CNN inference, a computationally intensive application, on resource-constrained devices. Through this edited volume we aim to provide a structured presentation of CNN enabled IoT applications in vision, speech, and natural language processing. This book discusses a variety of CNN techniques and applications, including but not limited to, IoT enabled CNN for speech de-noising, a smart app for visually impaired people, disease detection, ECG signal analysis, weather monitoring, texture analysis, etc. Unlike other books on the market, this book covers the tools, techniques, and challenges associated with the implementation of CNN algorithms, computation time, and the complexity associated with reasoning and modelling various types of data. We have included CNN's current research trends and future directions |
| Umfang: | 1 Online-Ressource (xlvi, 362 Seiten) Illustrationen, Diagramme |
| ISBN: | 9788770227124 8770227128 9781003393030 1003393039 9781000879711 1000879712 1000879690 9781000879698 |
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| 520 | 3 | |a Convolutional neural networks (CNNs), a type of deep neural network that has become dominant in a variety of computer vision tasks, in recent few years has attracted interest across a variety of domains due to their high efficiency at extracting meaningful information from visual imagery. Convolutional neural networks (CNNs) excel at a wide range of machine learning and deep learning tasks. As sensor-enabled internet of things (IoT) devices pervade every aspect of modern life, it is becoming increasingly critical to run CNN inference, a computationally intensive application, on resource-constrained devices. Through this edited volume we aim to provide a structured presentation of CNN enabled IoT applications in vision, speech, and natural language processing. This book discusses a variety of CNN techniques and applications, including but not limited to, IoT enabled CNN for speech de-noising, a smart app for visually impaired people, disease detection, ECG signal analysis, weather monitoring, texture analysis, etc. Unlike other books on the market, this book covers the tools, techniques, and challenges associated with the implementation of CNN algorithms, computation time, and the complexity associated with reasoning and modelling various types of data. We have included CNN's current research trends and future directions | |
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| id | DE-604.BV049486569 |
| illustrated | Illustrated |
| indexdate | 2025-01-29T17:02:00Z |
| institution | BVB |
| isbn | 9788770227124 8770227128 9781003393030 1003393039 9781000879711 1000879712 1000879690 9781000879698 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034831967 |
| oclc_num | 1418705002 |
| open_access_boolean | |
| owner | DE-573 DE-91 DE-BY-TUM |
| owner_facet | DE-573 DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xlvi, 362 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-37-RPEB ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf_2024 |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | River Publishers Routledge |
| record_format | marc |
| series2 | River Publishers series in automation, control and robotics |
| spellingShingle | IoT-enabled convolutional neural networks techniques and applications |
| title | IoT-enabled convolutional neural networks techniques and applications |
| title_auth | IoT-enabled convolutional neural networks techniques and applications |
| title_exact_search | IoT-enabled convolutional neural networks techniques and applications |
| title_full | IoT-enabled convolutional neural networks techniques and applications editors Mohd Naved, V. Ajantha Devi, Loveleen Gaur, Ahmed A. Elngar |
| title_fullStr | IoT-enabled convolutional neural networks techniques and applications editors Mohd Naved, V. Ajantha Devi, Loveleen Gaur, Ahmed A. Elngar |
| title_full_unstemmed | IoT-enabled convolutional neural networks techniques and applications editors Mohd Naved, V. Ajantha Devi, Loveleen Gaur, Ahmed A. Elngar |
| title_short | IoT-enabled convolutional neural networks |
| title_sort | iot enabled convolutional neural networks techniques and applications |
| title_sub | techniques and applications |
| url | https://ieeexplore.ieee.org/book/9997412 https://www.taylorfrancis.com/books/9781003393030 https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3619637 |
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